AI News, NIPS Proceedingsβ

NIPS Proceedingsβ

Part of: Advances in Neural Information Processing Systems 27 (NIPS 2014) We propose a new framework for estimating generative models via adversarial nets, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G.

When point releases of the individual projects accumulate to a critical mass, or if there is a critical bug in one of them that needs to be available to everyone, the release train will push out “service releases” with names ending “-SRX”, where “X” is a number.

Put this code inside a Spring Boot application with spring-boot-starter-data-jpa like this: Launch your app and Spring Data (having been autoconfigured by Boot, SQL or NoSQL) will automatically craft a concrete set of operations: On top of the CRUD operations inherited from CrudRepository, the interface defines two query methods.

Get Ready for Core ML 2

Core ML 2 lets you integrate a broad variety of machine learning model types into your app.

In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models.

Supported features include face tracking, face detection, landmarks, text detection, rectangle detection, barcode detection, object tracking, and image registration.

An ensemble learning framework for anomaly detection in building energy consumption

During building operation, a significant amount of energy is wasted due to equipment and human-related faults.

To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energy-saving procedures.

To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework.

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